Modelling of Supercapacitors: Factors Influencing Performance

The utilizable capacitance of Electrochemical Double Layer Capacitors (EDLCs) is a function of the frequency at which they are operated and this is strongly dependent on the construction and physical parameters of the device. We simulate the dynamic behavior of an EDLC using a spatially resolved model based on the porous electrode theory. The model of Verbrugge and Liu (J. Electrochem. Soc. 152, D79 (2005)) was extended with a dimension describing the transport into the carbon particle pores. Our results show a large influence of the electrode thickness (Le), separator thickness (Ls) and electrolyte conductivity (κ) on the performance of EDLCs. In agreement with experimental data, the time constant was an increasing function of Le and Ls and a decreasing function of κ. The main limitation was found to be on the scale of the whole cell, while transport into the particles became a limiting factor only if the particle size was unrealistically large. The results were generalized into a simplified relation allowing for a quick evaluation of performance for the design of new devices. This work provides an insight into the performance limitation of EDLCs and identifies the critical parameters to consider for both systems engineers and material scientists

An awkward looking pilomatrixoma

Pilomatrixoma is a rare and benign skin tumor of the hair follicle that tends to develop in the head and neck area. We report a case and briefly review the literature.peer-reviewe

Multi-temperature state-dependent equivalent circuit discharge model for lithium-sulfur batteries

Lithium-sulfur (Li-S) batteries are described extensively in the literature, but existing computational models aimed at scientific understanding are too complex for use in applications such as battery management. Computationally simple models are vital for exploitation. This paper proposes a non-linear state-of-charge dependent Li-S equivalent circuit network (ECN) model for a Li-S cell under discharge. Li-S batteries are fundamentally different to Li-ion batteries, and require chemistry-specific models. A new Li-S model is obtained using a ‘behavioural’ interpretation of the ECN model; as Li-S exhibits a ‘steep’ open-circuit voltage (OCV) profile at high states-of-charge, identification methods are designed to take into account OCV changes during current pulses. The prediction-error minimization technique is used. The model is parameterized from laboratory experiments using a mixed-size current pulse profile at four temperatures from 10 °C to 50 °C, giving linearized ECN parameters for a range of states-of-charge, currents and temperatures. These are used to create a nonlinear polynomial-based battery model suitable for use in a battery management system. When the model is used to predict the behaviour of a validation data set representing an automotive NEDC driving cycle, the terminal voltage predictions are judged accurate with a root mean square error of 32 mV

Diagnosing health in composite battery electrodes with explainable deep learning and partial charging data

Lithium-ion batteries with composite anodes of graphite and silicon are increasingly being used. However, their degradation pathways are complicated due to the blended nature of the electrodes, with graphite and silicon degrading at different rates. Here, we develop a deep learning health diagnostic framework to rapidly quantify and separate the different degradation rates of graphite and silicon in composite anodes using partial charging data. The convolutional neural network (CNN), trained with synthetic data, uses experimental partial charging data to diagnose electrode-level health of tested batteries, with errors of less than 3.1% (corresponding to the loss of active material reaching ∼75%). Sensitivity analysis of the capacity-voltage curve under different degradation modes is performed to provide a physically informed voltage window for diagnostics with partial charging data. By using the gradient-weighted class activation mapping approach, we provide explainable insights into how these CNNs work; highlighting regions of the voltage-curve to which they are most sensitive. Robustness is validated by introducing noise to the data, with no significant negative impact on the diagnostic accuracy for noise levels below 10 mV, thus highlighting the potential for deep learning approaches in the diagnostics of lithium-ion battery performance under real-world conditions. The framework presented here can be generalised to other cell formats and chemistries, providing robust and explainable battery diagnostics for both conventional single material electrodes, but also the more challenging composite electrodes.</p

Assessing and comparing German and UK transition policies for electric mobility

AbstractThis paper presents a novel policy assessment approach for sustainable transitions using insights from the multilevel perspective (MLP). An analysis of current German and UK policies for sustainable transport is conducted to illustrate its application. For both cases a potential transition pathway, that can satisfy environmental protection and industrial competitiveness goals, is derived from archetypal transition pathways. These are then put in relation to current policies, discussing whether these measures support these pathways. In the UK case, where emission reduction goals and industrial development are pursued together, current policies of promoting the diffusion of electric vehicles as well as industrial niches are supporting the emergence of a reconfiguration pathway. Replacing foreign suppliers, the local automotive industry shall become a significant part of the future regime. In contrast to that, Germany focuses on a careful transformation and conservation of its automotive industry where none of the current actors is left behind

Novel Methods for Measuring the Thermal Diffusivity and the Thermal Conductivity of a Lithium-Ion Battery

Thermal conductivity is a fundamental parameter in every battery pack model. It allows for the calculation of internal temperature gradients which affect cell safety and cell degradation. The accuracy of the measurement for thermal conductivity is directly proportional to the accuracy of any thermal calculation. Currently the battery industry uses archaic methods for measuring this property which have errors up to 50 %. This includes the constituent material approach, the Searle’s bar method, laser/Xeon flash and the transient plane source method. In this paper we detail three novel methods for measuring both the thermal conductivity and the thermal diffusivity to within 5.6 %. These have been specifically designed for bodies like lithium-ion batteries which are encased in a thermally conductive material. The novelty in these methods comes from maintaining a symmetrical thermal boundary condition about the middle of the cell. By using symmetric boundary conditions, the thermal pathway around the cell casing can be significantly reduced, leading to improved measurement accuracy. These novel methods represent the future for thermal characterisation of lithium-ion batteries. Continuing to use flawed measurement methods will only diminish the performance of battery packs and slow the rate of decarbonisation in the transport sector

Structures of smooth muscle myosin and heavy meromyosin in the folded, shutdown state

Remodelling of the contractile apparatus within smooth muscle cells is an essential process that allows effective contractile activity over a wide range of cell lengths. The thick filaments may be redistributed via depolymerisation into inactive myosin monomers that have been detected in vitro, in which the long tail has a folded conformation. The structure of this folded molecule has been controversial. Using negative stain electron microscopy of individual folded molecules from turkey gizzard we show they are more compact than previously described, with heads and the three segments of the folded tail closely packed. Smooth muscle heavy meromyosin (HMM), which lacks two-thirds of the tail, closely resembles the equivalent parts of whole myosin. Image processing reveals a characteristic head region morphology for both HMM and myosin whose features are identifiable by comparison with less compact molecules. The two heads associate asymmetrically: the tip of one motor domain touches the base of the other, resembling the blocked and free heads of this HMM when it forms 2-D crystals on lipid. The tail of HMM lies between the heads, contacting the blocked motor domain, unlike in the 2-D crystal. The tail of the intact myosin is bent sharply and consistently at two positions close to residues 1175 and 1535. The first bend position correlates with a skip in the coiled coil sequence, the second does not. The first segment runs between the heads from the head-tail junction. Unexpectedly, the other segments associate only with the blocked head rather than both heads, such that the second bend lies at a specific position near the C-lobe of the blocked head regulatory light chain. Quantitative analysis of tail flexibility shows that the single coiled coil of HMM has an apparent Young’s modulus of about 0.5 GPa. The folded tail of the intact molecule is less flexible indicating interactions between the segments. The folded tail does not modify the compact head arrangement but stabilises it, indicating a structural mechanism for the very low ATPase activity of the folded molecule

Insights into the role of silicon and graphite in the electrochemical performance of silicon/graphite blended electrodes with a multi-material porous electrode model

Silicon/graphite blended electrodes are promising candidates to replace graphite in lithium ion batteries, benefiting from the high capacity of silicon and the good structural stability of carbon. Models have proven essential to understand and optimise batteries with new materials. However, most previous models treat silicon/graphite blends as a single “lumped” material, offering limited understanding of the behaviors of the individual materials and thus limited design capability. Here, we present a multi-material model for silicon/graphite electrodes with detailed descriptions of the contributions of the individual active materials. The model shows that silicon introduces voltage hysteresis to silicon/graphite electrodes and consequently a “plateau shift” during delithiation of the electrodes. There will also be competition between the silicon and graphite lithiation reactions depending on silicon/graphite ratio. A dimensionless competing factor is derived to quantify the competition between the two active materials. This is demonstrated to be a useful indicator for active operating regions for each material and we demonstrate how it can be used to design cycling protocols for mitigating electrode degradation. The multi-material electrode model can be readily implemented into full-cell models and coupled with other physics to guide further development of lithium ion batteries with silicon-based electrodes